Proceedings of the International AAAI Conference on Web and Social Media最新文献_第2页

Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War between Ukraine and Russia 冲突时期的推文:追踪推特关于乌克兰和俄罗斯战争话语的公共数据集

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22208

Emily Chen, Emilio Ferrara

{"title":"Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War between Ukraine and Russia","authors":"Emily Chen, Emilio Ferrara","doi":"10.1609/icwsm.v17i1.22208","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22208","url":null,"abstract":"On February 24, 2022, Russia invaded Ukraine. In the days that followed, reports kept flooding in from laymen to news anchors of a conflict quickly escalating into war. Russia faced immediate backlash and condemnation from the world at large. While the war continues to contribute to an ongoing humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in the online space, both in the use of social media to garner support for both sides of the conflict and also in the context of information warfare. In this paper, we present a collection of nearly half a billion tweets, from February 22, 2022, through January 8, 2023, that we are publishing for the wider research community to use. This dataset can be found at https://github.com/echen102/ukraine-russia. Our preliminary analysis on a subset of our dataset already shows evidence of public engagement with Russian state-sponsored media and other domains that are known to push unreliable information towards the beginning of the war; the former saw a spike in activity on the day of the Russian invasion, while the other saw spikes in engagement within the first month of the war. Our hope is that this public dataset can help the research community to further understand the ever-evolving role that social media plays in information dissemination, influence campaigns, grassroots mobilization, and much more, during a time of conflict.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135910219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Lady and the Tramp Nextdoor: Online Manifestations of Real-World Inequalities in the Nextdoor Social Network 邻家小姐与流浪汉:邻家社交网络中现实世界不平等的在线表现

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22155

Waleed Iqbal, Vahid Ghafouri, Gareth Tyson, Guillermo Suarez-Tangil, Ignacio Castro

引用次数: 0

Information Retention in the Multi-Platform Sharing of Science 科学多平台共享中的信息保留

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22153

Sohyeon Hwang, Emőke-Ágnes Horvát, Daniel M. Romero

{"title":"Information Retention in the Multi-Platform Sharing of Science","authors":"Sohyeon Hwang, Emőke-Ágnes Horvát, Daniel M. Romero","doi":"10.1609/icwsm.v17i1.22153","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22153","url":null,"abstract":"The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention, we develop a keyword-based computational measure comparing an online post to the scientific article's abstract. We evaluate our measure using ground truth data labeled by within field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin in social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time---posing a critical concern for researchers, policymakers, and citizens alike---but suggest that multi-platform discussions may improve information retention overall.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136041098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks InfluencerRank:通过图卷积关注递归神经网络发现有效的影响者

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22162

Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han, Wei Wang

{"title":"InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks","authors":"Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han, Wei Wang","doi":"10.1609/icwsm.v17i1.22162","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22162","url":null,"abstract":"As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136041286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reddit in the Time of COVID 新冠疫情时期的Reddit

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22196

Veniamin Veselovsky, Ashton Anderson

{"title":"Reddit in the Time of COVID","authors":"Veniamin Veselovsky, Ashton Anderson","doi":"10.1609/icwsm.v17i1.22196","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22196","url":null,"abstract":"When the COVID-19 pandemic hit, much of life moved online. Platforms of all types reported surges of activity, and people remarked on the various important functions that online platforms suddenly fulfilled. However, researchers lack a rigorous understanding of the pandemic's impacts on social platforms---and whether they were temporary or long-lasting. We present a conceptual framework for studying the large-scale evolution of social platforms and apply it to the study of Reddit's history, with a particular focus on the COVID-19 pandemic. We study platform evolution through two key dimensions: structure vs. content and macro- vs. micro-level analysis. Structural signals help us quantify how much behavior changed, while content analysis clarifies exactly how it changed. Applying these at the macro-level illuminates platform-wide changes, while at the micro-level we study impacts on individual users. We illustrate the value of this approach by showing the extraordinary and ordinary changes Reddit went through during the pandemic. First, we show that typically when rapid growth occurs, it is driven by a few concentrated communities and within a narrow slice of language use. However, Reddit's growth throughout COVID-19 was spread across disparate communities and languages. Second, all groups were equally affected in their change of interest, but veteran users tended to invoke COVID-related language more than newer users. Third, the new wave of users that arrived following COVID-19 was fundamentally different from previous cohorts of new users in terms of interests, activity, and likelihood of staying active on the platform. These findings provide a more rigorous understanding of how an online platform changed during the global pandemic.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136041289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Just Another Day on Twitter: A Complete 24 Hours of Twitter Data 又是Twitter上的一天:完整的Twitter 24小时数据

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22215

Jürgen Pfeffer, Daniel Matter, Kokil Jaidka, Onur Varol, Afra Mashhadi, Jana Lasser, Dennis Assenmacher, Siqi Wu, Diyi Yang, Cornelia Brantner, Daniel M. Romero, Jahna Otterbacher, Carsten Schwemmer, Kenneth Joseph, David Garcia, Fred Morstatter

{"title":"Just Another Day on Twitter: A Complete 24 Hours of Twitter Data","authors":"Jürgen Pfeffer, Daniel Matter, Kokil Jaidka, Onur Varol, Afra Mashhadi, Jana Lasser, Dennis Assenmacher, Siqi Wu, Diyi Yang, Cornelia Brantner, Daniel M. Romero, Jahna Otterbacher, Carsten Schwemmer, Kenneth Joseph, David Garcia, Fred Morstatter","doi":"10.1609/icwsm.v17i1.22215","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22215","url":null,"abstract":"At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135909939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War on Reddit 偶然:利用语义搜索追踪俄罗斯国家媒体在Reddit上关于俄罗斯-乌克兰战争的叙述

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22149

Hans W. A. Hanley, Deepak Kumar, Zakir Durumeric

{"title":"Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War on Reddit","authors":"Hans W. A. Hanley, Deepak Kumar, Zakir Durumeric","doi":"10.1609/icwsm.v17i1.22149","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22149","url":null,"abstract":"In the buildup to and in the weeks following the Russian Federation’s invasion of Ukraine, Russian state media outlets output torrents of misleading and outright false information. In this work, we study this coordinated information campaign in order to understand the most prominent state media narratives touted by the Russian government to English-speaking audiences. To do this, we first perform sentence-level topic analysis using the large-language model MPNet on articles published by ten different pro-Russian propaganda websites including the new Russian “fact-checking” website waronfakes.com. Within this ecosystem, we show that smaller websites like katehon.com were highly effective at publishing topics that were later echoed by other Russian sites. After analyzing this set of Russian information narratives, we then analyze their correspondence with narratives and topics of discussion on r/Russia and 10 other political subreddits. Using MPNet and a semantic search algorithm, we map these subreddits’ comments to the set of topics extracted from our set of Russian websites, finding that 39.6% of r/Russia comments corresponded to narratives from pro-Russian propaganda websites compared to 8.86% on r/politics.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135910223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

HateMM: A Multi-Modal Dataset for Hate Video Classification 仇恨视频分类的多模态数据集

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22209

Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, Animesh Mukherjee

{"title":"HateMM: A Multi-Modal Dataset for Hate Video Classification","authors":"Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, Animesh Mukherjee","doi":"10.1609/icwsm.v17i1.22209","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22209","url":null,"abstract":"Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even lesser on videos. Thus, early stage automated video moderation techniques are needed to handle the videos that are being uploaded to keep the platform safe and healthy. With a view to detect and remove hateful content from the video sharing platforms, our work focuses on hate video detection using multi-modalities. To this end, we curate ~43 hours of videos from BitChute and manually annotate them as hate or non-hate, along with the frame spans which could explain the labelling decision. To collect the relevant videos we harnessed search keywords from hate lexicons. We observe various cues in images and audio of hateful videos. Further, we build deep learning multi-modal models to classify the hate videos and observe that using all the modalities of the videos improves the overall hate speech detection performance (accuracy=0.798, macro F1-score=0.790) by ~5.7% compared to the best uni-modal model in terms of macro F1 score. In summary, our work takes the first step toward understanding and modeling hateful videos on video hosting platforms such as BitChute.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135912560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

This Sample Seems to Be Good Enough! Assessing Coverage and Temporal Reliability of Twitter’s Academic API 这个样本似乎足够好!评估Twitter学术API的覆盖范围和时间可靠性

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22182

Jürgen Pfeffer, Angelina Mooseder, Jana Lasser, Luca Hammer, Oliver Stritzel, David Garcia

{"title":"This Sample Seems to Be Good Enough! Assessing Coverage and Temporal Reliability of Twitter’s Academic API","authors":"Jürgen Pfeffer, Angelina Mooseder, Jana Lasser, Luca Hammer, Oliver Stritzel, David Garcia","doi":"10.1609/icwsm.v17i1.22182","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22182","url":null,"abstract":"Because of its willingness to share data with academia and industry, Twitter has been the primary social media platform for scientific research as well as for consulting businesses and governments in the last decade. In recent years, a series of publications have studied and criticized Twitter's APIs and Twitter has partially adapted its existing data streams. The newest Twitter API for Academic Research allows to \"access Twitter's real-time and historical public data with additional features and functionality that support collecting more precise, complete, and unbiased datasets. The main new feature of this API is the possibility of accessing the full archive of all historic Tweets. In this article, we will take a closer look at the Academic API and will try to answer two questions. First, are the datasets collected with the Academic API complete? Secondly, since Twitter's Academic API delivers historic Tweets as represented on Twitter at the time of data collection, we need to understand how much data is lost over time due to Tweet and account removal from the platform. Our work shows evidence that Twitter's Academic API can indeed create (almost) complete samples of Twitter data based on a wide variety of search terms. We also provide evidence that Twitter's data endpoint v2 delivers better samples than the previously used endpoint v1.1. Furthermore, collecting Tweets with the Academic API at the time of studying a phenomenon rather than creating local archives of stored Tweets, allows for a straightforward way of following Twitter's developer agreement. Finally, we will also discuss technical artifacts and implications of the Academic API. We hope that our work can add another layer of understanding of Twitter data collections leading to more reliable studies of human behavior via social media data.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135912563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

"This Is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information “这是假新闻”:描述推特用户对COVID-19虚假信息的自发揭穿

Proceedings of the International AAAI Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22176

Kunihiro Miyazaki, Takayuki Uchiba, Kenji Tanaka, Jisun An, Haewoon Kwak, Kazutoshi Sasahara

{"title":"\"This Is Fake News\": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information","authors":"Kunihiro Miyazaki, Takayuki Uchiba, Kenji Tanaka, Jisun An, Haewoon Kwak, Kazutoshi Sasahara","doi":"10.1609/icwsm.v17i1.22176","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22176","url":null,"abstract":"False information spreads on social media, and fact-checking is a potential countermeasure. However, there is a severe shortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics like COVID-19. In this study, we focus on spontaneous debunking by social media users, which has been missed in existing research despite its indicated usefulness for fact-checking and countering false information. Specifically, we characterize the tweets with false information, or fake tweets, that tend to be debunked and Twitter users who often debunk fake tweets. For this analysis, we create a comprehensive dataset of responses to fake tweets, annotate a subset of them, and build a classification model for detecting debunking behaviors. We find that most fake tweets are left undebunked, spontaneous debunking is slower than other forms of responses, and spontaneous debunking exhibits partisanship in political topics. These results provide actionable insights into utilizing spontaneous debunking to scale conventional fact-checking, thereby supplementing existing research from a new perspective.","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136041109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0